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In this article, the eye-in-hand visual trajectory tracking control problem of wheeled mobile robots (WMRs) is considered. Different from the conventional vision-based approaches, the monocular camera is not required to be mounted at the center of WMR, and thus the derived visual model is subject to not only the nonholonomic constraint but also the unknown camera extrinsic parameters. To ensure the WMR can track a desired trajectory effectively, a combined observation/control strategy is proposed. First, a concurrent learning observer is designed to identify the camera extrinsic parameters with measurable visual signals. Then, with the aid of the estimated parameters, a nonlinear controller is presented to achieve the tracking task. The closed-loop system stability is analyzed with Lyapunov methods, showing that both the estimation and tracking errors are asymptotically convergent to zero. Simulation and experimental results are provided to validate the developed approach. [ABSTRACT FROM AUTHOR]

This paper presents a road reconstruction algorithm based on two-view geometry. A general geometric model for both planar and nonplanar scenes is proposed with respect to a reference plane. Then, points in the scene can be described geometrically by the projective parallax with respect to the reference plane. In practice, the grounding plane of the vehicle is selected as the reference plane. A row-wise image registration method is proposed to estimate the parallax information effectively. Based on the geometric model, points in the scene can be reconstructed as relative position and height information with respect to the reference plane. For road detection, the scene can be segmented where the height changes abruptly. Then, the road region is obtained by a diffusion strategy. The road surface can be reconstructed using the proposed model. Experiments are conducted to show the effectiveness and robustness of the proposed approach. The proposed approach is general in the sense that it does not rely on the priors of road appearance and structure, and it is not restricted to specific stereo camera configurations making it extendable to other multicamera systems. [ABSTRACT FROM AUTHOR]

In this paper, a 2-point algorithm is proposed to estimate the relative pose as well as the absolute scale between two vehicle-mounted cameras efficiently. The system model is deduced by combining a two-view geometric model and the planar motion constraint to reduce the degrees of freedom. 2-point correspondences are utilized to calculate the rotation information independently from the translation information, which indicates that the proposed algorithm can deal with pure rotation scenes. Besides, provided that the camera’s configuration satisfies a certain condition, the absolute scale can be recovered. An approximation algorithm is developed and combined with the random sample and consensus scheme to deal with the uneven ground surfaces in practice. As only 2-point correspondences are required, less iterations are demanded in the estimating procedure compared with many other existing related algorithms. Both simulation and experiments are implemented to evaluate the proposed algorithm, in which the synthetic data, virtual robot experimentation platform, KITTI Vision Benchmark, and SUMMIT-XL platform are acquired. According to the results, the proposed algorithm performs better than many related algorithms including the well-known 5-point algorithm in many cases, especially when the camera’s trajectory contains sharp corners. [ABSTRACT FROM AUTHOR]